A new process was designed, work instructions were modified, and the staff was trained.
Experience has also shown us that the more complex the process change, the higher probability of failure. Problematic changes may include order management, order to expenditure, sales, and operations planning processes. People tend to prefer ideas and procedures in which they see real value at first glance. If the value of the complete proposal doesn’t jump out at them, they are likely to adopt only the parts they believe will bring immediate value. They will lean toward implementing processes that demand less effort and labor or that will help them meet KPIs that result in bonuses.
Hard experience has shown me that, to ensure lasting benefit, process change must be anchored by digital technology. Full Article
Data is a critical company asset that has continued to grow exponentially for over a decade. According to a recent white paper by research firm Frost & Sullivan, 2.5 exabytes of data are created daily ("Why Embedded Analytics is the Future of Data Utilization").
However, to create game-changing business value, data not only needs to be analyzed, but it needs to be utilized. BI has long been delivered through dashboards that promised self-service, but adoption rates remain low among business users. Years of training and positive intent have failed to create the widespread embrace of insights that companies need in order to survive and thrive in a changing business world. How can we rethink the dashboard for modern business needs which require companies to be more agile and innovative for both their internal employees and their customers in a rapidly changing business environment? Full Article
MIT Sloan School of Management
The COVID-19 pandemic has disrupted everything from consumer behavior to supply chains, and the economic fallout is causing further changes. The data analytics field faces a complicated problem: how to use past data, and predict future behavior, in the face of uncertainty. Few organizations are facing business as usual or as expected.
“It’s hard to get good data about the future, so we have to use data from the past,” said Thomas Davenport, a professor at Babson University and fellow at the MIT Initiative on the Digital Economy. “And if the past is no longer a guide to the future, we’re going to have a tough time doing any sort of predictive analytics.” Full Article